Demographics

Everyone

Demographics for all included participants.

Demographics
Summary
N Age (years) Education (years) Sex (M/F/O) EHI
844 29.08 (6.03) 14.38 (2.48) 441/391/12 5.09 (79.37)



Race n
White 606
Black or African American 82
Multiple 76
Asian 69
American Indian or Alaska Native 5
Native Hawaiian or Other Pacific Islander 3
Other 3


Hispanic ethnicity n
No 744
Yes 100


By handedness group

Demographics for included participants, by handedness group (EHI bins).

Handedness N Age (years) Education (years) Sex (M/F/O) EHI
Left 331 28.84 (6.1) 14.45 (2.39) 170/157/4 -81.61 (19.27)
Mixed 135 28.83 (6.17) 14.58 (2.6) 77/56/2 -8.89 (26.49)
Right 378 29.38 (5.93) 14.24 (2.5) 194/178/6 86.01 (16.61)
Left: (EHI <= -40) | Mixed: (-40 < EHI < 40) | Right: (EHI >= 40)


Field x Level x Handedness (binned)

Do we find an interaction of field x level x handedness, when handedness is binned as left (EHI < -40) or right (EHI > +40)?

Summary. For reaction time, we find the critical interaction in the predicted direction (11.67ms, 95%CI [0.65, 22.69], p = .019, one-sided). I haven’t run accuracy yet, but the effect will be close to zero, opposite the predicted direction (the point estimates are 1.76 for righties, 1.96 for lefties).

Reaction time

Plots

Error bars show 95% CI.



Statistics

Simple mixed regression model

Reaction time is modeled as a linear effect of field, level, and handedness, using data from every target-present trial with a “go” response:

lmer( rt ~ field*level*handedness + (1 | subject) )


Field by level by handedness interaction (RT)
ANOVA: compare models with vs. without interaction term
npar AIC BIC logLik deviance Chisq Df p.value1
9 1,166,282.858 1,166,367.139 −583,132.429 1,166,264.858 - - -
10 1,166,280.551 1,166,374.197 −583,130.276 1,166,260.551 4.307 1 .038
1 F-test (Two-sided?)


Field by level interaction (RT)
Omnibus F-test
term df sumsq meansq statistic p.value
field 1 1,664,691.722 1,664,691.722 23.612 <.0001
level 1 9,626,122.373 9,626,122.373 136.54 <.0001
handedness 1 10,185,712.46 10,185,712.46 144.477 <.0001
field:level 1 2,730,949.837 2,730,949.837 38.737 <.0001
field:handedness 1 1,505,316.949 1,505,316.949 21.352 <.0001
level:handedness 1 12,247.994 12,247.994 0.174 .677
field:level:handedness 1 127,954.685 127,954.685 1.815 .178
Residuals 86,205 6,077,502,195.866 70,500.576 - -


Field by level by handedness interaction (RT)
Compare effect estimate to zero with emmeans()
field_consec level_consec handedness_consec estimate1 SE df2 asymp.LCL3 asymp.UCL3 z.ratio p.value4
LVF - RVF Local - Global Right - Left 11.666 5.622 Inf 0.648 22.685 2.075 .038
1 A positive number means LVF global bias is stronger in right handers (as predicted by AAH)
2 Z-approximation
3 Confidence level: 95%
4 Two-sided


LVF Global bias by handedness bin (RT)
field_consec level_consec handedness estimate1 SE df2 asymp.LCL3 asymp.UCL3 z.ratio p.value4
LVF - RVF Local - Global Left 15.641 4.096 Inf 7.613 23.669 3.819 .0001
LVF - RVF Local - Global Mixed 21.658 6.414 Inf 9.087 34.228 3.377 .0007
LVF - RVF Local - Global Right 27.307 3.829 Inf 19.802 34.812 7.131 <.0001
1 A positive number means global bias (faster RT for global)
2 Z-approximation
3 Confidence level: 95%
4 Two-sided, uncorrected


Accuracy

In progress.

Field x Level x Handedness (continuous)

Do we find an interaction of field x level x handedness (continuous EHI score)?

Summary. For reaction time, we find the critical interaction in the predicted direction (.067ms per EHI unit, or 14.82ms for EHI = -100, 28.14ms for EHI = +100. I haven’t figured out how to get confidence intervals around this estimate yet, but the one-sided p-value for an ANOVA model comparison is .020. I haven’t run the accuracy stats yet, but the subject-level scatter plot looks very flat.

Reaction time

Plots

Statistics

Model RT as a linear effect of field, level, and EHI (continuous):

rt_model_ehi <- lmer( rt ~ field*level*ehi + (1 | subject) )

Field by level by ehi interaction (RT)
ANOVA: compare models with vs. without interaction term
npar AIC BIC logLik deviance Chisq Df p.value1
9 1,387,640.958 1,387,726.807 −693,811.479 1,387,622.958 - - -
10 1,387,638.71 1,387,734.097 −693,809.355 1,387,618.71 4.248 1 .039
1 F-test (two-sided?)


Estimated global bias by field, for EHI of -100
contrast estimate1 SE df z.ratio p.value
(LVF Local ehi-100) - (LVF Global ehi-100) 29.412 3.009 Inf 9.776 <.0001
(RVF Local ehi-100) - (RVF Global ehi-100) 14.59 3.016 Inf 4.837 <.0001
1 Estimated global bias (ms)



Estimated LVF Global Bias for EHI of -100
LVF_global_bias
14.822


Estimated global bias by field, for EHI of +100
contrast estimate1 SE df z.ratio p.value
LVF Local ehi100 - LVF Global ehi100 36.074 2.824 Inf 12.775 <.0001
RVF Local ehi100 - RVF Global ehi100 7.93 2.83 Inf 2.802 .026
1 Estimated global bias (ms)


Estimated LVF Global Bias for EHI of +100
LVF_global_bias
28.144


\[ 28.144 - 14.822 = 13.322ms \\ 13.322/200 = 0.067ms / EHI unit \] Each unit change in EHI (-100:100) corresponds to a 0.067ms difference in LVF global bias. This is the slope estimate given by the summary function: 

summary(rt_model_ehi)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ field:level:ehi + field:level + field:ehi + level:ehi +  
##     field + level + ehi + (1 | subject)
##    Data: aah_for_rt_ehi_model
## 
## REML criterion at convergence: 1387626.2
## 
## Scaled residuals: 
##       Min        1Q    Median        3Q       Max 
## -4.879612 -0.590631 -0.167132  0.363313  7.653749 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subject  (Intercept) 28269.4  168.135 
##  Residual             42128.7  205.253 
## Number of obs: 102615, groups:  subject, 844
## 
## Fixed effects:
##                             Estimate  Std. Error   t value
## (Intercept)              680.1697334   5.9429525 114.44980
## fieldRVF                  -2.6755865   1.8307862  -1.46144
## levelGlobal              -32.7431087   1.8162558 -18.02781
## ehi                        0.2190687   0.0747656   2.93007
## fieldRVF:levelGlobal      21.4828945   2.5694569   8.36087
## fieldRVF:ehi              -0.1228165   0.0230212  -5.33493
## levelGlobal:ehi           -0.0333102   0.0228331  -1.45886
## fieldRVF:levelGlobal:ehi   0.0666075   0.0323174   2.06104
## 
## Correlation of Fixed Effects:
##             (Intr) fldRVF lvlGlb ehi    flRVF:G flRVF: lvlGl:
## fieldRVF    -0.155                                           
## levelGlobal -0.156  0.506                                    
## ehi         -0.064  0.010  0.010                             
## fldRVF:lvlG  0.110 -0.713 -0.706 -0.007                      
## fieldRVF:eh  0.010 -0.067 -0.033 -0.154  0.047               
## levelGlbl:h  0.010 -0.033 -0.065 -0.156  0.046   0.506       
## fldRVF:lvG: -0.007  0.047  0.046  0.110 -0.065  -0.712 -0.706

Test for a simple correlation between each subject’s EHI and LVF global bias.

Subject-level correlation: linear model
term estimate std.error statistic p.value1
(Intercept) 16.696 2.454 6.804 <.0001
ehi 0.042 0.031 1.374 .17
1 Two-sided


Subject-level correlation: Spearman's rho
rho statistic p.value1 method alternative
0.041 96,127,760.619 .119 Spearman's rank correlation rho greater
1 One-sided



Accuracy

Plots

Statistics

In progress. Model accuracy as a binomial effect of field, level, and EHI (continuous):

acc_ehi_model <- glmer( rt ~ field*level*ehi + (1 | subject), family = "binomial" )

Test for a simple correlation between each subject’s EHI and LVF global bias.

Subject-level correlation: linear model
term estimate std.error statistic p.value1
(Intercept) 1.987 0.311 6.381 <.0001
ehi 0.002 0.004 0.448 .655
1 Two-sided


Subject-level correlation: Spearman's rho
rho statistic p.value1 method alternative
0.036 96,571,310.453 .147 Spearman's rank correlation rho greater
1 One-sided